import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
x = stocks.date
y = stocks.GOOG
fig, ax = plt.subplots()
ax.plot(x,y)
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('Stock value')
plt.xticks(np.arange(0, len(x)+1, 10))
plt.xticks(rotation=45)
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
x = stocks.date
y1 = stocks.GOOG
y2 = stocks.AAPL
y3 = stocks.AMZN
fig, ax = plt.subplots()
ax.plot(x,y1)
ax.plot(x,y2)
ax.plot(x,y3)
ax.set_title('Value of stocks')
ax.set_xlabel('date')
ax.set_ylabel('Value')
plt.xticks(np.arange(0, len(x)+1, 10))
plt.xticks(rotation=45)
plt.legend(['Google', 'Apple', 'Amazon'])
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
#Does the size of the group have an impact on the total bill.
g = sns.FacetGrid(tips, col='sex', hue='size')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()
#Conclusion: Yes it does
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
df = stocks
fig = px.line(df, x="date", y = ['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'], title='Value of stock')
fig.show()
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
#Are there differences between male and female when it comes to giving tips
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y = "tip" , facet_col="sex")
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
df_2007 = df.query('year==2007')
fig = px.histogram(df_2007, x="pop", y="continent", color='continent', text_auto='TRUE')
fig.update_yaxes(categoryorder= 'total ascending')
fig.show()